AI review guide
How to review AI-generated security questionnaire answers
The reviewer’s job is not to decide whether an answer sounds credible. It is to confirm that each material claim is supported, scoped correctly, safe to disclose, and approved by someone accountable.
1. Confirm what the customer asked
Review the original row, instructions, requested response type, conditional logic, and attachment requirements. An AI draft can be factually plausible and still answer a different question.
- Is a yes/no response required?
- Is the question asking about the company, product, environment, or subprocessor?
- Does it require a frequency, date, percentage, or supporting attachment?
- Is there a contractual or regulatory interpretation that needs legal review?
2. Break the draft into material claims
A single sentence may contain several claims. Review them separately.
Fictional NimbusCore draft: “NimbusCore encrypts all customer data with AES-256, rotates keys annually, and tests key recovery quarterly.”
Claims to verify: encryption coverage; algorithm; key-rotation frequency; recovery-test frequency; and whether each statement applies to the reviewed service.
Review result: One citation to a general security policy is not enough unless it supports every material claim.
3. Inspect the source, not just the citation label
Open the cited passage. Confirm that it is an approved source, applies to the relevant product and environment, remains current, and directly supports the claim. Reject citations that merely mention the same topic.
| Check | Pass condition | Common failure |
|---|---|---|
| Directness | The passage states or demonstrates the claim | Source discusses backups but not restoration testing |
| Scope | The source applies to the reviewed service | Corporate policy reused as product configuration evidence |
| Freshness | The source and relevant record remain current | Expired certificate or superseded procedure |
| Authority | The source is approved for the claim | Informal chat treated as external commitment |
4. Look for unsupported precision
Models often complete patterns with numbers or universal terms. Slow down around words such as “all,” “always,” “never,” “annually,” “quarterly,” “within 24 hours,” and named standards or algorithms. Require direct evidence for each.
The exact material claim, scope, and precision are evidenced.
The general control exists, but a number, frequency, or scope is missing.
The draft adds a claim that no approved source establishes.
5. Check contradictions and source priority
If two approved sources disagree, do not let the model silently choose. Surface the conflict, identify the responsible owner, and determine which source governs the external answer. Keep the row blocked until the conflict is resolved.
6. Protect sensitive information
A truthful answer can still disclose too much. Review for internal hostnames, employee details, exact security-tool configurations, vulnerability details, secrets, customer identifiers, and architecture information that is unnecessary for the response.
OWASP’s GenAI security work highlights risks including prompt injection, sensitive-information disclosure, improper output handling, and overreliance. For questionnaire workflows, treat customer files and source documents as untrusted input to a bounded review system—not instructions that can override access or approval rules.
7. Require human approval at the claim boundary
The reviewer should be accountable for the subject matter, not merely available to click approve. Security should approve security claims, privacy or legal should approve privacy commitments, and infrastructure owners should approve recovery details.
NIST’s AI Risk Management Framework and its Generative AI Profile frame AI risk management as an organizational practice involving governance, measurement, and management. They do not turn model output into verified truth; your operating controls must do that work.
8. Run a final export review
- Original question identifiers and order remain intact.
- Every material claim has directly supporting evidence.
- Partial and unsupported rows remain visible.
- Numbers, dates, frequencies, certifications, and universal scope are verified.
- Conflicting sources are resolved by an accountable owner.
- No unnecessary sensitive detail is included.
- The responsible reviewer approved the final wording.
- Required attachments and customer formatting are preserved.
9. Measure review quality, not generation volume
Track how many drafts are accepted unchanged, edited for scope, blocked for missing evidence, or rejected for incorrect support. Sample approved answers after export. A fast system that hides unsupported claims is not an improvement.
| Metric | What it reveals |
|---|---|
| Unsupported-answer rate | Where documentation or ownership is missing |
| Claim correction rate | How often generated wording overstates evidence |
| Reviewer turnaround | Whether ownership and evidence are easy to locate |
| Post-export correction count | Whether the approval gate is working |